Mining multiple-level spatial association rules for objects with a broad boundary
Data & Knowledge Engineering
Object-Based Selective Materialization for Efficient Implementation of Spatial Data Cubes
IEEE Transactions on Knowledge and Data Engineering
CLARANS: A Method for Clustering Objects for Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
Spatial Clustering in the Presence of Obstacles
Proceedings of the 17th International Conference on Data Engineering
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
Clustering Spatial Data in the Presence of Obstacles: a Density-Based Approach
IDEAS '02 Proceedings of the 2002 International Symposium on Database Engineering & Applications
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Optimizing the size and locations of facilities in competitive multi-site service systems
Computers and Operations Research
A neural model for the p-median problem
Computers and Operations Research
A hybrid EM approach to spatial clustering
Computational Statistics & Data Analysis
Clustering of spatial point patterns
Computational Statistics & Data Analysis
Efficient discovery of multilevel spatial association rules using partitions
Information and Software Technology
Spatially enabled customer segmentation using a data classification method with uncertain predicates
Decision Support Systems
GIS enabled service site selection: Environmental analysis and beyond
Information Systems Frontiers
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
This article applies customer service to be the research background. Spatial data mining method is proposed to solve site selection of the service center. Firstly, a new data model for recording all the information of customer management is given, which transforms the traditional model-driven strategy to data-oriented method. Secondly, a hybrid spatial clustering method named OETTC-MEANS-CLASA algorithm is proposed. It has the advantages of applying k-means algorithm to reduce the result space and using simulated annealing method (CLASA) as result-searching strategy to find more qualified solutions. On the basis of GIS functions, we design deeper analytical function to take spatial obstacle factors, spatial environmental factors, spatial terrain factors, spatial traffic factors and cost factors into account. The result of the experiment declares that the algorithm does better at the both aspects of perform efficiency and result quality.